Browsing by Author "Cui, Xuelin"

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    Joint CT-MRI Image Reconstruction
    Cui, Xuelin (Virginia Tech, 2018-11-28)
    Modern clinical diagnoses and treatments have been increasingly reliant on medical imaging techniques. In return, medical images are required to provide more accurate and detailed information than ever. Aside from the evolution of hardware and software, multimodal imaging techniques offer a promising solution to produce higher quality images by fusing medical images from different modalities. This strategy utilizes more structural and/or functional image information, thereby allowing clinical results to be more comprehensive and better interpreted. Since their inception, multimodal imaging techniques have received a great deal of attention for achieving enhanced imaging performance. In this work, a novel joint reconstruction framework using sparse computed tomography (CT) and magnetic resonance imaging (MRI) data is developed and evaluated. The method proposed in this study is part of the planned joint CT-MRI system which assembles CT and MRI subsystems into a single entity. The CT and MRI images are synchronously acquired and registered from the hybrid CT-MRI platform. However, since their image data are highly undersampled, analytical methods, such as filtered backprojection, are unable to generate images of sufficient quality. To overcome this drawback, we resort to compressed sensing techniques, which employ sparse priors that result from an application of L₁-norm minimization. To utilize multimodal information, a projection distance is introduced and is tuned to tailor the texture and pattern of final images. Specifically CT and MRI images are alternately reconstructed using the updated multimodal results that are calculated at the latest step of the iterative optimization algorithm. This method exploits the structural similarities shared by the CT and MRI images to achieve better reconstruction quality. The improved performance of the proposed approach is demonstrated using a pair of undersampled CT-MRI body images and a pair of undersampled CT-MRI head images. These images are tested using joint reconstruction, analytical reconstruction, and independent reconstruction without using multimodal imaging information. Results show that the proposed method improves about 5dB in signal-to-noise ratio (SNR) and nearly 10% in structural similarity measurements compared to independent reconstruction methods. It offers a similar quality as fully sampled analytical reconstruction, yet requires as few as 25 projections for CT and a 30% sampling rate for MRI. It is concluded that structural similarities and correlations residing in images from different modalities are useful to mutually promote the quality of image reconstruction.
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    Sparse-Prior-Based Projection Distance Optimization Method for Joint CT-MRI Reconstruction
    Cui, Xuelin; Mili, Lamine M.; Yu, Hengyong (IEEE, 2017)
    Multimodal imaging techniques have received a great deal of attention, since their inceptions for achieving an enhanced imaging performance. In this paper, a novel joint reconstruction framework for computed tomography (CT) and magnetic resonance imaging (MRI) is implemented and evaluated. The CT and MRI data sets are synchronously acquired and registered from a hybrid CT-MRI platform. Because the image data sets are highly undersampled, the conventional methods (e.g., analytic reconstructions) are unable to generate decent results. To overcome this drawback, we employ the compressed sensing (CS) sparse priors from an application of discrete gradient transform. On the other hand, to utilize multimodal imaging information, the concept of projection distance is introduced to penalize the large divergence between images from different modalities. During the optimization process, CT and MRI images are alternately updated using the latest information from current iteration. The method exploits the structural similarities between the CT and MRI images to achieve better reconstruction quality. The entire framework is accelerated via the parallel processing techniques implemented on a nVidia M5000M Graph Processing Unit. This results in a significant decrease of the computational time (from hours to minutes). The performance of the proposed approach is demonstrated on a pair of undersampled projections CT and MRI body images. For comparison, the CT and MRI images are also reconstructed by an analytic method, and iterative methods with no exploration of structural similarity, known as independent reconstructions. Results show that the proposed joint reconstruction provides a better image quality than both analytic methods and independent reconstruction by revealing the main features of the true images. It is concluded that the structural similarities and correlations residing in images from different modalities are useful to mutually promote the quality of joint image reconstruction.